Automation and digitalization, as long-term evolutionary processes, cause significant effects, such as the transformation of occupations and job profiles, changes to employment forms, and a more significant role for the platform economy, generating challenges for social policy. This systematic literature review aims to provide an overview of the research to date related to influences of the Industry 4.0 Revolution on human capital development and consumer behavior. A search on the Web of Science identified 160 papers that met the inclusion criteria. The major objectives aimed to identify: the main types of influences of the Industry 4.0 Revolution on human capital development and consumer behavior; the main opportunities and challenges for new directions in education associated with shifting the work environment; and the drivers for human capital development and consumer behavior through the lenses of the Industry 4.0 Revolution. The results revealed some key aspects for the development of human capital: information, new jobs, the Internet, technology, training, education, new skills, automation, communication, innovativeness, professionals, productivity, artificial intelligence, digitalization, e-recruitment, and the Internet of Things, as well as the main drivers of consumer behavior: information, e-commerce, digitalization, the Internet of Things, e-distribution, technology, digitalization, automation, personalized, performance, artificial intelligence, behavior intention, e-shopping, and data mining.

European Union (EU) policy encourages the development of a blue economy (BE) by unlocking the full economic potential of oceans, seas, lakes, rivers and other water resources, especially in member countries in which it represents a low contribution to the national economy (under 1%). However, climate change represents a main barrier to fully realizing a BE. Enabling conditions that will support the sustainable development of a BE and increase its climate resiliency must be promoted. Romania has high potential to contribute to the development of the EU BE due to its geographic characteristics, namely the presence of the Danube Delta-Black Sea macrosystem, which is part of the Romanian Lower Danube Euroregion (RLDE). Aquatic living resources represent a sector which can significantly contribute to the growth of the BE in the RLDE, a situation which imposes restrictions for both halting biodiversity loss and maintaining the proper conditions to maximize the benefits of the existing macrosystem. It is known that climate change causes water quality problems, accentuates water level fluctuations and loss of biodiversity and induces the destruction of habitats, which eventually leads to fish stock depletion. This paper aims to develop an analytical framework based on multiple linear predictive and forecast models that offers cost-efficient tools for the monitoring and control of water quality, fish stock dynamics and biodiversity in order to strengthen the resilience and adaptive capacity of the BE of the RLDE in the context of climate change. The following water-dependent variables were considered: total nitrogen (TN); total phosphorus (TP); dissolved oxygen (DO); pH; water temperature (wt); and water level, all of which were measured based on a series of 26 physicochemical indicators associated with 4 sampling areas within the RLDE (Brăila, Galați, Tulcea and Sulina counties). Predictive models based on fish species catches associated with the Galati County Danube River Basin segment and the “Danube Delta” Biosphere Reserve Administration territory were included in the analytical framework to establish an efficient tool for monitoring fish stock dynamics and structures as well as identify methods of controlling fish biodiversity in the RLDE to enhance the sustainable development and resilience of the already-existing BE and its expansion (blue growth) in the context of aquatic environment climate variation. The study area reflects the integrated approach of the emerging BE, focused on the ocean, seas, lakes and rivers according to the United Nations Agenda. The results emphasized the vulnerability of the RLDE to climate change, a situation revealed by the water level, air temperature and water quality parameter trend lines and forecast models. Considering the sampling design applied within the RLDE, it can be stated that the Tulcea county Danube sector was less affected by climate change compared with the Galați county sector as confirmed by water TN and TP forecast analysis, which revealed higher increasing trends in Galați compared with Tulcea. The fish stock biodiversity was proven to be affected by global warming within the RLDE, since peaceful species had a higher upward trend compared with predatory species. Water level and air temperature forecasting analysis proved to be an important tool for climate change monitoring in the study area. The resulting analytical framework confirmed that time series methods could be used together with machine learning prediction methods to highlight their synergetic abilities for monitoring and predicting the impact of climate change on the marine living resources of the BE sector within the RLDE. The forecasting models developed in the present study were meant to be used as methods of revealing future information, making it possible for decision makers to adopt proper management solutions to prevent or limit the negative impacts of climate change on the BE. Through the identified independent variables, prediction models offer a solution for managing the dependent variables and the possibility of performing less cost-demanding aquatic environment monitoring activities.

The process of improving tourism requires prior determination of the existing offer, as well as assessment of the advantages and weaknesses of the given offer. Upon the analysis, it is possible to improve the tourist offer. This study examined ways in which agro-tourism can be improved, related to the foundation of sustainability. Methodologically, this article involves the use of expert assessment, additionally supported with fuzzy logic based on the fuzzy PIPRECIA and fuzzy MARCOS methods. Using the fuzzy PIPRECIA method, the criteria and sub-criteria were weighted, while the observed agro-tourism facilities in Bosnia and Herzegovina (BiH) were ranked according to the applied fuzzy MARCOS method. The results of the application of the fuzzy PIPRECIA method showed that for experts the most important are economic criteria, then environmental, and the latter important are social criteria. The most important sub-criterion is the quality of services. Out of the six facilities in question, the most appropriate results are achieved at the “Šadrvan” rural household, while the worst outcome is exhibited at the agro-tourism facility, (rural household) “Kovačević.” Sensitivity analysis confirmed these results. The aim of this article was to evaluate the agritourism offer in BiH, taking into consideration these six agritourism facilities. Based on that, it is necessary to determine on what advantages to build agritourism in BiH, and what should be corrected in order to be more competitive. Based on the obtained results, the facilities management should determine which sustainability criteria would need to be improved, as well as on which criteria it is desirable to build a competitive advantage, aiming to advance the tourist offer.

This study examines the impact of renewable energy, economic growth, and trade openness on CO₂ emissions in the EU-15 countries over the period 1980–2022, employing the ARDL modeling framework. In addition, a panel PMG-ARDL model is employed as a robustness check. The analysis identifies cointegration among the variables in 11 out of the 15 countries studied. Economic growth is found to increase CO₂ emissions, highlighting the ongoing challenge of aligning economic expansion with environmental objectives. The estimated coefficients for economic growth range from 0.43 to 5.70, depending on the country. Renewable energy significantly reduces emissions, highlighting its critical role in achieving sustainability (the corresponding coefficient moves in the range −0.13 to −0.96). Trade openness generally shows a neutral impact on emissions across most cases. Overall, renewable energy contributes to reducing CO₂ emissions, whereas the effects of economic growth and trade openness remain mixed and country-specific. These findings highlight the need to promote cleaner technologies, enhance energy efficiency, and ensure broader access to environmentally friendly energy sources.

Moldova possesses the largest area of farmland as a share of its total land surface, an advantage which should encourage economic development strategies oriented towards the agriculture sector. Government subsidies and agriculture loans have been used as tools for developing the Moldavian agriculture. However, considering the challenges generated by both climate change (the drought from year 2012 that affected 80% of farmland) and a difficult political situation (restrictions imposed by the Russian Federation on the Republic of Moldova’s agri-food imports and exports between 2013 and 2014), the country’s agricultural system ranks very low when it comes to agricultural production efficiency. The present paper analyses the performances of the agricultural sector and its impact on the Moldavian economy over a nine-year period (between 2008 and 2016), by using a custom-developed analytical framework based on a dataset containing 21 relevant indicators. The analytical framework generates various perspectives that can be used to elaborate an economic sustainable development strategy of the Moldavian agriculture sector. The development of the analytical framework is based on the dynamics of agriculture subsidies, agricultural loans, the agricultural sector’s gross domestic product (GDP) and gross value added (GVA), as well as the dynamics of agricultural production and production value, also considering the main crops belonging to the Moldavian agriculture sector. The results are presented as sets of mathematical regression models that quantify the relationships found between the relevant agricultural parameters and their impact on the economics of the agricultural sector. It has been identified that the agriculture sector has a considerable impact on the Moldavian economy, a fact revealed by the significant model between the agriculture GVA and total GVA and GDP. A significant, negative correlation model was identified between agriculture subsidies and agriculture loans, although a small percentage of Moldavian agriculture farms were subsidized. Strong correlation models were also identified between wheat and maize production and total agriculture production, emphasizing the importance of these two crops for the Moldavian agricultural economy. Grape and maize production values also generated a correlation model, emphasizing the market interconnection between these crops It can be concluded that the increase in value of governmental agriculture subsidies, as well as expanding their addressability in order to maximize the access possibility for a higher number of agriculture farms, are essential for the Moldavian agriculture sector’s future development, since considering the limiting value of and accessibility to subsidies, a direct correlation model was identified between governmental agriculture subsidies and agriculture GVA.

The period of the Covid 19 pandemic meant for the tourism industry a moment of general assessment of its own route. The plans and programs in which this industry was active at the time of the outbreak of the pandemic were closely related to the transformation of this industry into one that operates based on the principles of sustainability. The decrease in the number of arrivals, the practical stoppage for a period of this economic sector, attracted by itself determinations regarding the value volumes of the decreases in economic indicators but also research for the possibilities of further development of this industry. It can thus be noted that at the level of Romania, the decrease in the number of arrivals was over 50% in 2020 compared to 2019. In addition, although the forecasts of the World Tourism Organization indicated a relatively quick return in 2021, this did not happen in Romania, the value level being in 2021 at approximately 70% compared to 2019. The purpose of this article is to analyze the behavior of tourism demand and supply in the three periods, namely: pre-pandemic, pandemic and post-pandemic. The data are based on records from the National Institute of Statistics, as well as studies and research carried out on this topic. The conclusions of the determinations carried out are mainly related to the nature of isolated tourism observed during the pandemic, but also to the behavior of entrepreneurs in the sector who reacted immediately in the development of these areas.